Microbes such as viruses and bacteria are organized hierarchically. For example, a virus is constituted of atoms assembled into macromolecules which, in turn, constitute several substructures. For a non-enveloped virus, the latter are genetic material and the capsid. For an enveloped system, such as dengue virus, there is an outer protein net, a lipid zone, and an inner RNA-protein complex. Accompanying this hierarchical organization is a spectrum of time and length scales.
Modern nano-characterization experimental methodologies make the development of microbial simulation approaches timely. For example, atomic force microscopy (AFM) may be employed to investigate a range of biological processes from unfolding of a single molecule to nano-indentation of viruses. A standard AFM can scan a sample more than 10 thousand times per second, yielding an ensemble measurement that parallels a statistical mechanical approach. Thus, to model such experiments computationally, a framework is needed that addresses structures in a range of sizes from single macromolecules to viruses and bacteria, without losing information at any time or length scale.
Nano-technical methods for characterizing macromolecular assemblies include AFM, Ion Mobility—Mass Spectrometry, chemical labeling, and nano-pore measurements. While these techniques provide information on structure, they are coarse-grained, in that they do not resolve all-atom configurations. X-ray and electron microscopy provide detailed structure but do not provide information on dynamics. Solid-state nuclear magnetic resonance (NMR) techniques do provide an ensemble of atom-resolved structures but cannot be used to give overall structure for a macromolecular assembly. A method which integrates multiple types of nano-characterization data with a predictive all-atom simulation approach would greatly advance the understanding of microbial systems.
The above and other experimental techniques can be performed under various microenvironmental conditions such as salinity and pH. These variations modulate interactions between solvent accessible parts of the microbe and host medium atoms, inducing structural and functional changes of the former. An all-atom model is often essential to correctly probe these interactions. Structural fluctuations and internal dynamics are a central feature of many biological processes. For example, in the presence of an energy barrier, the atomic fluctuations allow self-organization of lipids in membranes. Fluctuations are also important in expressing the conformational diversity of macromolecules that allows for large deformations upon drug binding. Similarly, excessive fluctuations in viral epitopes appear to diminish immune response and may explain the dependence of immunogenicity on their fluctuations. Thus, an all-atom description is desirable to account for all sources of fluctuation in simulating the aforementioned processes, and hence has been the basis of traditional molecular dynamics (MD) approaches.
All-atom MD simulations of macromolecular assemblies involving more than a million atoms (such as a virus in an explicit solvation environment) require large computational capabilities and have been accomplished using more than 1000 processors for a single time-course. To simulate viruses over microseconds on such a platform would require engaging this many processors for months (assuming the usual femto-second MD timestep). This restricts traditional MD to less than 50 nm structures and hundred nano-second timescales. Hence, incorporating information about atomic processes into microbe modeling has been a challenge. Billion-atom MD simulations have been accomplished. However, these simulations neglect Coulomb interactions, bonded forces, or the rapidly fluctuating proton. All of these are central to biomolecular structure and dynamics.
Multiscale approaches have been developed to address the above computational challenges. As used herein, a “multiscale” method simultaneously accounts for processes on a range of scales. These methods may yield insights into the dynamics of a system as it simultaneously evolves across multiple scales in space and time. Existing approaches to multiscale modeling include Principal Component Analysis (PCA) modes to identify collective behaviors in macromolecular systems, dihedral angles, curvilinear coordinates to characterize macromolecular folding and coiling, bead models wherein a peptide or nucleotide is represented by a bead which interacts with others via a phenomenological force, and spatial coarse-grained models. The foregoing approaches, however, suffer from one or more of the following difficulties: (1) characteristic variables are not slowly varying in time; (2) macromolecular twist is not readily accounted for; (3) their internal dynamics, and hence inelasticity of their collisions is neglected; and (4) the forces involved must be calibrated for most new applications.